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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: lerobot
+pipeline_tag: robotics
+tags:
+- lerobot
+- robotics
+- manipulation
+- pi0
+- single-arm
+---
+
+# pi05_single — insert_01, step 2000
+
+openpi `pi05_single` checkpoint for a **single-arm, vision-only** insertion task.
+Step 2000 (1.13 epochs), held-out validation loss **0.1063**.
+
+This is the best checkpoint of the run -- validation loss got monotonically worse afterwards.
+
+## Configuration
+
+This run differs from the sibling `pi05_bi_*` models in two ways that make their
+loss values **not comparable**:
+
+| | `pi05_single` (this) | `pi05_bi` (two_tubes / task2 / bread) |
+|---|---|---|
+| state / action dim | 7 / 10 | bimanual dims |
+| image streams used | **1** (`camera0` only) | 6 (2 RGB + 4 tactile) |
+| lr schedule | constant 2e-4 after 1k warmup | cosine 2.5e-5 -> 2.5e-6 |
+
+The dataset ships six image streams (`camera0`, `camera1`, and four tactile
+sensors) but `pi05_single` reads only `camera0`; `camera1` and all tactile inputs
+are discarded.
+
+## Data
+
+`KaiyueChen/insert_01`: 500 episodes / 252,795 frames, LeRobot v2.1, 30 fps,
+images embedded in the parquet files.
+
+**One frame per episode was dropped during preparation.** The collection pipeline
+writes an all-zero action vector on each episode's terminal frame (it has no
+successor action). Because the action vector contains two quaternions whose `w`
+components sit at ~0.99999 with a q01-q99 spread of only 2e-4, quantile
+normalization mapped those zeros to about -10,000, and the step-0 training loss
+came out at **193,510** while validation read 1.78 (validation always draws the
+same leading batches and happened to miss the terminal frames). Dropping the
+terminal frame removes 500 of 252,795 frames (0.2%), all of them invalid, and
+brings step-0 training loss to 0.74. Merged frame count: 252,295.
+
+Split, held out per source (10%, seed 42): train 450 / val_seen 50 / val_unseen 50.
+Normalization statistics (quantile q01/q99) over `train` only.
+
+## Training
+
+| | |
+|---|---|
+| config | `pi05_single` |
+| hardware | 2 x A100-80GB, FSDP |
+| batch size | 128 |
+| this checkpoint | step 2000 (~1.13 epoch; 1 epoch = 1,764 steps) |
+| run length | 6,000 steps (3.40 epochs), 4h08m at 2.4 s/it |
+| lr | **constant 2e-4** after 1,000 warmup steps |
+| | (`pi05_single` sets `peak_lr = decay_lr = 2e-4`, so the cosine schedule does not actually decay) |
+| LoRA | rank 16 on the LLM, rank 32 on the action expert |
+| vision tower | **fully fine-tuned** -- the freeze filter matches only `.*llm.*` |
+
+## Validation curve
+
+Flow-matching loss, 20 batches per split, same leading batches every evaluation.
+
+| step | train | val_seen | val_unseen | gap |
+|---:|---:|---:|---:|---:|
+| 0 | 0.7447 | 0.7284 | 0.7377 | +0.0093 |
+| **2000** | 0.0987 | 0.0860 | **0.1063** | +0.0203 |
+| 4000 | 0.0881 | 0.0750 | 0.1164 | +0.0414 |
+| 5999 | 0.0813 | 0.0673 | 0.1192 | +0.0519 |
+
+`val_unseen` was already at its minimum by the first validation point (step 2000,
+1.13 epochs) and got monotonically worse afterwards, while `val_seen` improved
+monotonically. The gap grew from +0.0203 to +0.0519 (+156%) across three
+consecutive points -- an unambiguous overfitting signature, not sampling noise.
+
+For contrast, the sibling six-input `pi05_bi` runs on this codebase turned much
+later and ended with far smaller gaps: two_tubes at ~2.5 epochs (+0.0220), task2
+at ~2 epochs (+0.0041), bread at ~2.4 epochs (-0.0037). This run, with a single
+camera and no tactile input, turned before 1.1 epochs and ended an order of
+magnitude wider. The most likely explanation is the reduced observation space:
+with one view and no touch, memorizing training trajectories is an easier way to
+lower the loss than learning a transferable policy. `pi05_single_vitac`
+(`camera0` + two tactile streams) would be the natural comparison, and insertion
+is a task where contact feedback plausibly matters.
+
+## Contents
+
+```
+checkpoint/
+  params/                      # inference weights
+  train_state/                 # optimizer state, for resuming
+  assets/insert_01/
+    norm_stats.json            # computed over the train split only
+```